STEMbot: A Compliant Robot for Under-Canopy Plant Navigation

2026-07-08

AI Summary

This study addresses the challenge that existing agricultural robots struggle to effectively monitor early-stage pests in occluded regions such as the undersides of leaves and stems. To overcome this limitation, the authors propose STEMbot, a miniature climbing robot that achieves, for the first time, autonomous navigation and high-fidelity 3D reconstruction within the complex branching structures of plants. The system integrates geometric PIN-SLAM with semantic OcTree mapping to represent the environment, employs a manifold-constrained A* path planner coupled with ray-casting-based goal selection to accurately inspect occluded areas, and features a compliant mechanical design adaptable to stems ranging from 7 to 33 mm in diameter. Experiments demonstrate that STEMbot operates robustly across four plant species, achieving a Chamfer distance below 1 cm in 3D reconstruction—significantly surpassing the current limitation of climbing robots that are restricted to unbranched main stems.

Abstract

The scalability of organic agriculture is partially limited by the labor costs associated with monitoring for pests. While drones and rovers are well-suited for agricultural monitoring from above or next to plants, many pests live on the underside of leaves or on plant stems, making them detectable only after they have caused significant damage. To enable early pest detection we present STEMbot, a miniature climbing robot system designed for autonomous navigation under plant canopies. Unlike existing climbing platforms that lack on-board perception or are restricted to unbranched vertical trunks, STEMbot integrates a fully geometric PIN-SLAM pipeline with a semantic OcTree to achieve robust localization and mapping while climbing the plant. To plan STEMbot's motion we propose a manifold-constrained A* planner along with ray-tracing goal specification to enable branch-aware traversal and the inspection of occluded targets. We validate our system through hardware experiments, demonstrating reliable traversal of stems ranging from 7-33mm and autonomous navigation across four distinct plant specimens. Quantitative evaluations show that our system achieves high-fidelity geometric reconstructions with an average Chamfer distance of less than 1cm relative to an offline photogrammetry baseline, confirming that STEMbot maintains the globally consistent odometry needed for autonomous navigation.

Problem

• early pest detection
• under-canopy navigation
• plant monitoring
• autonomous climbing robot

Innovation

• compliant climbing robot
• PIN-SLAM
• semantic OcTree
• manifold-constrained A*
• under-canopy navigation